Pellegrino Casoria, Simon Pietro Romano · 2026 · arXiv
DOI: 10.48550/arXiv.2610.31573 · open-access PDF
在API之后的环境中,社交媒体配置文件已经变得廉价和自动化:大型语言模型(LLMs)可以从非结构化内容中提取结构化信息,并推断用户从未披露的属性。一个人的真实曝光不是他们发布的内容,而是可以从中推断的内容。随着这些做法变得常规,暴露于社交工程攻击成为一个运营确定性,需要一个共享的评估和缓解框架。我们通过量化目标可以精确配置的程度,使这种暴露能够量化,并使社交配置如何实现。
Machine translation (Meta M2M100); the English original follows.
In the post-API landscape, social-media profiling has become inexpensive and automated: Large Language Models (LLMs) can extract structured information from unstructured content and deduce attributes users never disclosed. A person's true exposure is not what they post but what can be inferred from it. As these practices become routine, exposure to social engineering attacks becomes an operational certainty, demanding a shared framework for assessment and mitigation. We make this exposure measurable by quantifying how accurately a target can be profiled, and how far that profile enables social
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